Method for evaluating a frequency spectrum

ABSTRACT

A method evaluates a frequency spectrum representative of at least one time-dependent signal, the at least one time dependent signal being derived from an output from a measuring device under predetermined measuring device operating conditions. The time-dependent signal, includes a portion being representative of a wanted signal, and a portion being representative of noise. The method includes the steps of determining, based on the frequency spectrum of the signal, a value representative of the noise floor, identifying, based on the frequency spectrum of the signal derived under the predetermined operating condition, a peak component, and if the peak component satisfies a relative peak criterion determined on the basis of the determined value representative of the noise floor, determining the wanted signal by applying a predetermined algorithm. The invention further relates to a method for determining flow of a vortex measuring device, and a vortex sensor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119 ofEuropean Application 16 193 922.8, filed Oct. 14, 2016, the entirecontents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to a method for evaluating a frequency spectrum, amethod for determining flow of a vortex measuring device, and a vortexsensor for carrying out the method for evaluating a frequency spectrum.

BACKGROUND OF THE INVENTION

Measuring devices, as for example, vortex flow meters are known in priorart for measuring flow velocity of liquids or gases in pipelines.Further, there are known so-called multivariable vortex meters whichoffer a couple of advantages over regular vortex flow meters.Specifically, these multivariable vortex meters are temperature andpressure compensated, thereby offering more precise results and enablingdirect measurement of mass flow in fluids flowing through a pipeline.

Basically, a multivariable vortex meter may be configured to measureflow, relative pressure and temperature, for example, by means of a3-in-one sensor with a microelectromechanical system chip (MEMS). Thissensor chip has two measuring components: 1. A Wheatstone bridge of fourresistors with piezo-resistive effect at a pressure sensitive membrane.If a differential pressure is applied, the membrane will bend, and thefour resistors in the Wheatstone bridge are subject to mechanical stressand will change resistance with two resistors decreasing resistance andtwo resistors increasing resistance. Under application of a DC voltage,the Wheatstone bridge will yield a pressure dependent voltage output. 2.A resistor with a Temperature Coefficient of Resistance (TCR) changingdue to the temperature of the MEMS.

In order to measure flow, relative pressure and temperature of a fluidflowing through a pipeline by means of only one MEMS element, the sensormay only have one open port at the positive MEMS side, as used inrelative pressure sensors. At the negative pressure side, atmosphericpressure is applied from the inside of the sensor housing.

However, for measuring flow by means of vortex meters, usually atwo-port sensor is implemented being arranged downstream of the bluffbody. The velocity of the vortices causes small pressure changes, andwhen passing the two-port sensor, the latter will measure a smalldifferential pressure. While vortices will pass the sensor in analternating manner with respect to the positive and negative pressureports, the signal measured by the sensor will be a small sine signalcomprising some noise from hydraulic or other noise sources. The numberof the vortices generated per second is proportional to the flow so thatthe flow algorithm has to determine the frequency of the sine andmultiply it with a factor to obtain a value for the flow in the desiredunit.

The use of two-port sensors in vortex flow meters has the ad-vantagethat a rather good sine signal comprising little noise is obtained fromthe sensor. With low flow, the sine amplitude will have very lowamplitude, but the relative pressure (namely, pressure related toatmospheric pressure) in the vortex tube will comprise hydraulic noiseas harmonic noise components and/or large and rapid pressure deviations.Thus, there is a disadvantage with respect to the configurationdescribed above that the two-port pressure sensor is only sensitive todifferential pressure but not to relative pressure, because the two-portsolution effectively protects the sine signal from being altered by thehydraulic noise of the relative pressure.

Nevertheless, the above described two-port solution is able to measureboth flow and temperature. In order to be able to additionally measurerelative pressure by means of the above mentioned MEMS, the two-portsensor housing has to be replaced by a one-port sensor housing.

However, when using a one-port housing, the sensor will also besensitive to hydraulic noise emanating from the hydraulic relativepressure noise. Thus, the resulting sine curve will be modified ordistorted due to that noise. In fact, since the characteristics andmagnitude of the hydraulic noise differ from application to application,and even will differ over time within the same application, thisunpredictable noise poses a severe problem to measuring vortex flow withonly one pressure port.

Also other measuring devices, as for example, thermal flow meterscomprising a plurality of temperature sensors for measuring a thermalprofile around a heated body have to deal with the problem ofunpredictable noise. With respect to such applications, it is necessaryto measure very small temperature differences. Noise, for example, froman electronic circuit, can be problematic as well as noise caused by themedium.

SUMMARY OF THE INVENTION

Therefore, the present invention is based on an object to provide amethod for measuring, managing, and reducing noise during a measurementswhich are disturbed by relatively high noise. This object is solvedaccording to the present invention by a method for evaluating afrequency spectrum representative of at least one time-dependent signaland a method for determining flow of a vortex measuring device, and avortex sensor for carrying out the method.

Accordingly, a method for evaluating a frequency spectrum representativeof at least one time-dependent signal is provided, the at least one timedependent signal being derived from an output from a measuring deviceunder predetermined measuring device operating conditions, the measuringdevice comprising at least two sensors, the time dependent signalcomprising a portion being representative of a wanted signal, and aportion being representative of noise, wherein the method comprises thesteps of —determining, based on the frequency spectrum of the signal arepresentative of the noise floor, —identifying, based on the frequencyspectrum of the signal derived under the predetermined operatingcondition, a peak component, and —if the peak component satisfies arelative peak criterion determined on the basis of the determinedrepresentative of the noise floor, determining the wanted signal byapplying a predetermined algorithm.

By the inventive method, for the implementation of which a measuringdevice with more than one sensor is employed, measurements which aredisturbed by relatively high noise and very small local differencessmaller than the noise between the individual sensors can be carried outreliably. The inventive method enables measurements adaptive to thecurrent noise which makes an auto noise reduction by an averagingprocedure adapted to the current situation which only delivers ameasurable difference, if there is a statistically significantdifference. By the inventive method, it is possible to measure verysmall differences that normally cannot be measured when the signal is“buried” in or mixed by noise. The inventive method applies noisereduction that is only activated when there is a need for it, thus, thenoise reduction is adaptive and fits the specific noise. In applicationswhere the noise level changes from time to time, the inventive methodcan be used to secure that no ensuring is biased due to noise. When thenoise is low, the resolution is high, and when the noise is high, theresolution is low. The inventive method ensures that the measurement isset to zero whenever there is no statistical significance that it doesnot have a value greater than zero.

Further, according to the present invention, a method is provided,wherein the measuring device is a vortex measuring device, and whereinthe time-dependent signal comprises a portion being representative of aflow, and a portion being representative of noise. Thus, a reliableprocedure for distinguishing between noise component and a vortexcomponent in a spectrum, in particular, generated at low flow rates isprovided rendering correct results for any application of a vortexsensor implemented for flow measurement in a pipe. In particular, theinventive method is very well suited to be used in connection withvortex meters comprising a one-port housing so as to be able todetermine, even at low flow rates, relative pressure, temperature andflow with only one MEMS.

Alternatively, the measuring device may be a thermal flow metercomprising a plurality of temperature sensors for measuring a thermalprofile around a heated body. Also, in this kind of application, it isnecessary to measure the very small temperature differences and thisembodiment enables measuring very low flow. In contrast to mostflowmeter principles, the method according to this preferred embodimentis useful by employing an adaptive noise reduction.

According to a preferred embodiment of the method, a value for the noisefloor is calculated as an average of a number of selected noiserepresentative frequency spectrum components. Thereby, the accuracy ofthe results is increased substantially.

According to another preferred embodiment of the inventive method, thenoise representative frequencies spectrum components are selected amongfrequency components below a predefined frequency value, wherein thenoise representative frequencies spectrum components are the remainingcomponents after selecting a predetermined number of high valuefrequency components, wherein the high value frequency components are apredetermined number of frequency components with the highest amplitudevalues. Preferably, those selected noise representative frequenciesspectrum components below a predefined frequency value may be averagedto determine the noise floor.

Preferably, a relative peak signal is determined from the differencebetween the noise floor and the wanted signal, wherein the relative peaksignal is larger than or equal to the relative peak criterion.

It is advantageous, if the predetermined algorithm is adaptive withrespect to the noise by —always calculating the relative peak criterionas a function of the noise being present, and —selecting a suitableaveraging procedure securing a stable wanted signal output, inparticular selecting an averaging procedure with the least requirednumber of averaging runs based on the current noise floor.

Thereby, an intelligent adaptive solution is provided being suitable andproviding reliable results for any kind of application. In order to keepresponse time for the averaging procedure as low as possible, it isparticularly advantageous to keep the number of averaging runs at aminimum required depending on the noise floor. The higher the noisefloor is, the more averaging runs may be required to identify a signalin the noise with the desired confidence level. The lower the noisefloor is, the fewer averaging runs may be required to identify a signalin the noise with the desired confidence level. Therefore, response timemay be minimized by selecting a suitable averaging procedure dependingon the current noise floor. The selected number of averaging runs maydepend on the noise floor in such a manner that the number of averagingruns is the lowest integer sufficiently high to ensure that the there isa statistically significant difference between the noise and the wantedsignal, i.e. a signal can be differentiated from the noise at apre-determined confidence level. Furthermore, the relative peakcriterion may be adapted to the noise floor and/or to the number ofaveraging runs and/or the number of selected noise representativefrequencies spectrum components. On the one hand, the relative peakcriterion can be released for a selected averaging procedure with ahigher number of averaging runs at a higher noise floor, in particularat low flow, in order to be able to identify lower flow signals. On theother hand, the relative peak criterion can be stricter for a selectedaveraging procedure with a lower number of averaging runs at a lowernoise floor, in particular at high flow, in order to reduce the risk ofmisidentifying noise peaks as high flow signals.

The frequency spectrum may be calculated by averaging frequency spectraof a predetermined number of time dependent signals derived from theoutput of the measuring device. This increases the accuracy of theresults even further.

Further, a method for determining flow of a vortex measuring device isprovided, comprising the step of computing a sequence of frequencyspectra, each frequency spectrum of the sequence being computed by themethod described above, wherein for the first spectrum of the sequence,the predetermined number is 1, and for each subsequent spectrum of thesequence, the predetermined number is increased by a predeterminedpositive integer value, and wherein for each frequency spectrum of thesequence of frequency spectra, the corresponding flow is determined byapplying the predetermined algorithm, and evaluating if at least oneflow is determined to be greater than the noise floor, then the firstspectrum of the sequence of spectra where the determined flow is greaterthan the noise floor, is selected as basis for calculating the flow ofthe vortex measuring device, otherwise determining the flow to be zeroflow.

Preferably, each frequency spectrum is divided into a number of discretefrequency components, and identifying the peak component comprises thestep of deriving the peak by interpolating based on several frequencycomponents.

According to a preferred embodiment, each frequency spectrum of thesequence of frequency spectra is numbered with a consecutivelyincreasing channel number, the first frequency spectrum being numberedwith the smallest channel number, the last frequency spectrum beingnumbered with the largest channel number, wherein frequency spectrahaving higher channel numbers being averaged over frequency spectra ofpredetermined numbers of time dependent signals with lower frequencylimits than frequency spectra of predetermined numbers of time dependentsignals, having lower channel numbers, wherein prior to the evaluatingstep, for each frequency spectrum of the sequence of frequency spectra,the corresponding flow is determined by applying the predeterminedalgorithm, and if the flow of the spectrum having the smallest channelnumber is determined to be greater than the noise floor, that spectrumis selected as basis for calculating a steady state flow of the vortexmeasuring device, otherwise if the flow of the spectrum having thesmallest channel number is determined to be equal to the noise floor andat least one of the spectra having a channel number between the smallestand the largest channel number having a flow greater than the noisefloor, one of the spectra having a flow greater than the noise floor isselected as a basis for calculating a dynamic flow of the vortexmeasuring device, otherwise performing the evaluating step.

The method may further comprise a step of determining a stationary flowand a dynamic flow.

Preferably, the output signal is a relative pressure signal.

Moreover, it is advantageous, if the vortex measuring device comprises avortex sensor which is arranged within a one-port housing.

Further, according to the invention, a vortex sensor is provided beingadapted to carry out the method according to the invention.

Further details and features of the invention as well as concreteembodiments of the invention may be derived from the followingdescription in connection with the drawings. The various features ofnovelty which characterize the invention are pointed out withparticularity in the claims annexed to and forming a part of thisdisclosure. For a better understanding of the invention, its operatingadvantages and specific objects attained by its uses, reference is madeto the accompanying drawings and descriptive matter in which preferredembodiments of the invention are illustrated.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1A, is a graph showing a spectrum comprising a vortex component at28 Hz and showing a frequency spectrum being obtained after one run;

FIG. 1B is a graph showing a spectrum comprising a vortex component at28 Hz and showing a frequency spectrum being obtained after averagingover sixteen runs;

FIG. 2A is a graph showing the development of respective components fromthe frequency spectrum after different numbers of averaging proceduresand showing a vortex frequency component;

FIG. 2B is a graph showing the development of respective components fromthe frequency spectrum develop after different numbers of averagingprocedures and showing a noise frequency component;

FIG. 3 is a table for the basic selection of average channels;

FIG. 4 is a high resolution frequency spectrum which illustrates anexample for limiting the frequency area of average channels; and

FIG. 5 is a table according to which steady state flow and dynamic floware distinguished from a pattern of two valid peaks by using all averagechannels.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the drawings, FIG. 1A and FIG. 1B illustrate how the noisereduction system according to the present invention works on the basisof a frequency spectrum comprising a vortex component at 28 Hz.

Prior to explaining FIG. 1A and FIG. 1B, however, the following basicexplanations concerning measurements of flow, relative pressure, andtemperature are given below.

The temperature measurement has its own TCR (Temperature Coefficient ofResistance) component with respect to the MEMS. The flow measurement andthe measurement of the relative pressure share the same Wheatstonebridge located at the pressure sensitive membrane of the MEMS. TheWheatstone bridge outputs the differential pressure applied across theMEMS. This pressure consists of an alternating signal (AC) to be usedfor the flow sensor application, and a steady state signal (DC) for themeasurement of pressure, or a slow changing DC signal for themeasurement of relative pressure.

For the flow algorithm the AC signal is used but the DC signal, however,is not suited. Thus, a digital high pass filter provides a separatesignal channel for the flow algorithm with only the AC signal and the DCsignal being removed.

The pressure algorithm uses the DC signal, but here, the AC signal isnot suited. Thus, a digital low pass filter provides a separate signalchannel for the pressure algorithm with only the DC signal and the ACsignal being removed.

Then, the relative pressure measured has to be compensated for dynamicpressure, because the diameter of the pipe through which the flow to bemeasured occurs has another diameter at location at which the MEMS isarranged.

Further, the basis measurement with respect to flow algorithm is ameasurement of the frequency of the vortex generated sine. The method,which is chosen in the embodiment described here, is based on acalculation of the frequency spectrum of the AC signal. This is adiscrete spectrum comprising, e.g., 100 components spaced apart fromeach other by 4 Hz. On this basis, since each frequency will “see” 4 Hzto both sides, any frequency of a sine will be detected by one or twofrequency components. If the vortex consists, for example, of one puresine of e.g., 80 Hz and having an amplitude of 100 (e.g., 100 AD countof e.g., 1 mV, an amplitude of 100 mV), then the frequency components of80 Hz in the spectrum will output the amplitude of 100, and all othercomponents will provide an output of zero. If, e.g., noise of 30 Hz andan amplitude of 12 is also present, then the frequency component for 12Hz will provide the output of the value 12 as well.

Usually, a typical frequency spectrum will output a value above zero forthe noise floor for all frequency components, even when the flow iszero. This effect results from electrical and hydraulic noise. When flowis applied to the flow meter, then a frequency component for the vortexsine will be output having a value above the value the noise floor has.Further, also some noise components may be present with a value higherthan the value of the noise floor. Thus, the task of the flow algorithmin the sense of software is to analyze the frequency spectrum in orderto be able to distinguish between the vortex sine component and thenoise components. Since the vortex sine frequency flow characteristic isknown, the latter is able to effectively detect and reject most of thenoise components.

The nature of noise within the frequency spectrum can be de-scribed asoutlined below. If there is no flow, then the output of all frequencycomponents will be a random value higher or less than a mean value. Themeans value represents the noise floor and the random behavior is almosta normally distributed function. Then, the random behavior can bedescribed by a single parameter, namely, the standard spread or just thesigma, 68% of the random output lies within one sigma and 99,7% lieswithin 3 sigma.

The noise amount and characteristic varies from application toapplication at the customer side, and so do noise floor and sigma.However, the sigma is represented by a certain known function of thenoise floor so that when measuring the noise floor, then sigma may becalculated. Since the noise floor is measured, then, the actual noisefloor and the actual sigma of the noise are also known for eachapplication.

If the application involves high hydraulic noise, then this noise will“eat” the lower frequency components. In this case, the minimum flowcannot be measured and the system avoids detecting noise as flow.

Now, the basics for finding and approving the peak for the vortex willbe described. First, the flow is analyzed with respect to the frequencyspectrum so as to find the highest peak respectively meeting absolutepeak criteria and relative peak criteria. The absolute peak criteriaserve for verifying that the found peak is not a random noise signal butrather matches the characteristics of a vortex signal. The relativepeak, which verifies that the found signal is not a random sign isdetermined by determining the difference between the maximum peak whichhas been found and the noise floor, whereby it has to have a minimumvalue, which is adopted to the actual sigma. The noise floor iscalculated as the average of the peaks surrounding spectrum components.The frequency component being tested and its two neighbors are notincluded in the noise floor measurement, since the noise floor thenwould be higher than the correct value for a vortex component beingtested.

The following description is given for explaining how to distinguishbetween the flow and noise. The basic principle for distinguishingbetween components comprising noise and the components needed for theevaluation (“good” components) starts at a condition of zero flow atwhich only noise being present, and at which the sigma is highest. Thefrequency spectrum component responds to noise with the noise floor(mean value) and the dispersion, sigma, which is calculated as describedabove. The flow algorithm, therefore, will always include both, noisefloor and sigma. First, the flow algorithm is based on a zero flowhypothesis test. If the component output lies within the noise sigma(e.g., 3 sigma), this means that the relative peak criteria is not met,but the zero flow hypothesis is met with the flow being zero. Otherwise,if the absolute and relative peak criteria are met, then the frequencycomponent output refers to flow.

The basic concept of the zero flow hypothesis in summary is as follows:The noise floor is measured, the sigma of the noise is calculated, andthe relative peak is calculated by input of a confidence level andsigma. The zero flow hypothesis is selected with a confidence level(e.g. 3 sigma), thereby making it unlikely that noise is able to triggeran output above the relative peak criteria when flow is zero. If anoutput exceeds the relative peak criteria, it is likely that flow ispresent. In this case, flow will be detected, if the absolute peakcriteria also are met.

The appropriate level of the relative peak criteria is the sigma beingmultiplied by a factor providing the desired confidence (e.g. a factor 3will provide 3 sigma as confidence level). This is configurable.However, if the confidence level is set to be higher, then the relativepeak criteria will increase. Thus, if sigma increases, the relative peakcriteria increase too. The relative peak criteria respond to the actualnoise in the application so that the hypothesis test parameters actuallyadopt to the actual noise in the application. This provides for areliable and effective procedure to distinguish between “too noisecomponents” and “good components” in an unpredictable application.

Now, the basics of the noise reduction system will be described. Thenoise is reduced by averaging the frequency components over several runsN. When averaging, the sigma σ of the average result is reduced to thevalue σ/√{square root over (N)}. When averaging, for example, over aseries of four measurements, the sigma of the result is reduced to half,whereas averaging over a series of sixteen measurements reduces sigma to¼. Both components and the noise floor are averaged.

FIG. 1A shows a spectrum for only one run and vortex component at 28 Hz,whereas FIG. 1B shows a spectrum for an average of sixteen runs and avortex component at 28 Hz. After separating the 28 Hz vortex componentfrom the noise, the relative peak value, which in FIG. 1B is alsoreduced to ¼ compared to FIG. 1A, now allows the 28 Hz vortex componentto always be selected. By repeating the calculation of the frequencycomponents and averaging the latter, the sigma of the dispersion isreduced. This, however, only is necessary in the low flow range, asalready explained above. Channels of an average of 2, 4, 8, and 16 areavailable, whereby the last channel has ¼ sigma of the channel withoutaveraging.

The relative peak criteria are calculated for each channel including thesigma and noise floor measurement as well as the sigma of each channel.The sigma of the noise floor is rather low since it is measured by sixdifferent components reducing the sigma to 0.4 * sigma. Each channel hasits own noise floor due to different averaging.

With respect to FIG. 1A, the frequency spectrum is shown without anaveraging procedure for a vortex of 28 Hz. The upper light grey coloredportion of each column represents the respective portion of noise, whichin the example of FIG. 1A is rather large. Thus, regarding the highestpeak at a frequency of 28 Hz, it cannot be determined with certaintywhether this corresponds to the vortex. The relative peak criteria isonly suitable for distinguishing between the 28 Hz vortex component andthe surrounding noise components so as to avoid a misinterpretation of anoise peak for the vortex peak. However, here, the 28 Hz peak has a lowprobability to be selected, thus, the flow algorithm, without theaverage channel system and without the adaptive relative peak criteria,will indicate zero flow most of the time and will only be able todetermine the correct 28 Hz vortex component for about ⅕ of timesaccording to standard probability.

In contrast, in FIG. 1B the frequency components are averaged sixteentimes, thereby improving the detection of the vortex componenet in thespectrum. In this example, the vortex component will be determinedcorrectly from the spectrum with a 100% probability.

The selection of the average channel is tested from the “no-averagechannel”, followed by the “average-of-2-channel”, and so on up to thelast “average-of-16-channel”. Then, the first channel detecting flow isselected. If no channel at all detects flow, then the flow is determinedto be zero.

The above described procedure is very reliable due to the averaging, andthus, there is no risk to select a noise component wrongly instead ofthe desired vortex component from the spectrum.

The noise reduction system, thereby, is adaptive both to noise of eachapplication and to the appropriate amount of averaging.

FIG. 2A shows how a vortex frequency component develops according to thenumber of averaging procedures, and FIG. 2B shows how a noise frequencycomponent develops over the number of averaging procedures.

It should be noted that the sine signal amplitude, generated by thevortices, increase by a magnitude of two with flow. As noise usuallywill be rather constant in magnitude, the ratio of signal to noise (S/N)is very low, thus resulting in a noise signal with low flow. However, itwill quickly increase with flow, and the sine curve will be free fromcritical noise rather fast. Therefore, the problem with respect to noisefocusses to the lowest area of the flow range.

In FIG. 2A, as an example, the 28 Hz component shown in FIG. 1B is used,whereby the 28 Hz component, the relative peak criteria, and the noisefloor spread or dispersion are shown as a function of the number ofaveraging procedures, starting from 1 to 16 runs. Here, the average of16 is the only one which enables a correct determination of the vortexcomponent. It should be noted that the knowledge that a vortex componentwill always have lower noise dispersion than a noise componentcontributes to the reliability of the hypothesis test.

FIG. 2B shows the spectrum at zero flow. Also here, the noise frequencycomponent, the relative peak criteria and the noise floor spread ordispersion are shown as a function of the number of averagingprocedures. The range of noise with the desired confidence level isbelow the relative peak criteria at all averaging levels so as toprovide the same confidence for zero flow output for every averaginglevel.

FIG. 3 shows a table for the basic selection of average channels. In thetable, a value of “0” means no valid peak in the average channel. Incontrast, in the table, a value of “1” indicates a valid peak in theaverage channel. A value of “X” means, that the latter should not beconsidered. A valid peak can be defined as output which meets theabsolute peak criteria and the relative peak criteria as well. Theselected average channel here is indicated by the grey color. This basicselection prioritizes the lowest average channels. However, a moreadvantageous selection will be described below.

The vortex frequency is calculated as an interpolated value of severalfrequency components within the spectrum. This provides an accurateresult at any vortex frequency. This interpolating accuracy will evenincrease with increasing averaging runs, when flow is in steady state,since the components comprise less noise and will have lower tolerance.Thus, preferably, the channels with high averaging should be used, ifpossible a higher resolution is needed at low flow and thus a relativelyhigh noise floor. However, the channels with low averaging should beused to keep response time at a minimum. A preferred compromise is hereto select the averaging procedure with the least number of averagingruns required to identify a signal in the noise with the desiredconfidence level.

The minimum noise sets a minimum limit for the frequency for eachaverage channel. This minimum noise will not be able to provide thelowest frequency component in the spectrum being valid for eachmeasurement. Thus, this channel is not allowed to measure the lowestflow area. The “average-of-two-channel” is able to reliably determinelower frequencies than the “no-average-channel” and therefore representsthe lower limit. The same applies for all other average channels. The“average-of-sixteen-channel” starts at the lowest frequency.

FIG. 4 shows a high resolution frequency spectrum which illustrates anexample for limiting the frequency area of average channels. Here, thelowest frequency in the spectrum is component 3 at 20 Hz, whereascomponents 0, 1, and 2 at 8 Hz, 12 Hz, and 16 Hz, respectively, arebelow the frequency range of the vortex frequency range. The individuallimit of the frequency range for the average channels provides forbetter interpolation of frequency accuracy and better suppression ofsporadic noise.

Even better results and accuracy as well as an even better suppressionof sporadic noise can be achieved by limiting the step from one averagechannel to the next one, and especially the step over more than onechannel. This is of particular relevance with respect to steady stateflow, and in those cases, where an “average-of-16-channel” has to beused. In this case, sporadic valid peaks of the low average channels canbe avoided to be selected. This procedure is implemented in theso-called intelligent average channel selection described below andillustrated in FIG. 5.

FIG. 5 shows a table according to which “steady state flow” and “dynamicflow” may be distinguished from a pattern of two valid peaks by usingall average channels, thereby providing an intelligent average channelselection procedure according to the respective state. Namely, at steadystate flow, the “average-of-16-channel” will show valid peaks, but nodynamic flow. The table of FIG. 5 shows the patterns to be detected. Atable value of “0” indicates that no valid peak is present for anycomponent in the respective average channels. A table value of “1”indicates that a valid peak is present for at least one component in therespective average channels, whereas the value “X” indicates that thisshould not be taken into consideration. A valid peak is defined asoutput which meets the absolute peak criteria and the relative peakcriteria. The cells of the table which are shaded in grey indicate theselected average channels.

At steady state flow, the “average-of-16-channel” is selected tosuppress sporadic noise and high accuracy. However, if at least threelower average channels above also have valid peaks, then the“average-of-8-channel” is selected to enable faster response.

At dynamic flow, the “average-of-2-channel” is selected in order tosuppress sporadic noise and still provide fast response time. If,however, a pattern which is not included in the table shown in FIG. 5 ispresent, then the selection of default average channels as illustratedin the table shown in FIG. 3 is to be applied. However, the aboveprocedure described above with respect to FIG. 5 compared to theprocedure described with respect to FIG. 3 has the advantage that byalways selecting a larger averaging here in contrast to always selectthe smallest averaging (FIG. 3), an output having less dispersion/spreadis obtained. Accordingly, the result is more resistant against large andsporadic noise pulses that may “cheat/trick” the lowest averagedchannels. However, this advantage comes at the cost of a slower responsetime needed for larger averaging.

Thus, the above described procedure provides an intelligent noisereduction means integrated into the sensor algorithm for measuring,managing, and reducing noise when carrying out a measurement with ameasuring device having more than one senor. As already mentioned above,the method according to the present invention is suitable formeasurement devices as vortex measurement devices or thermal flowmeters. However, also other measurement devices having a plurality ofsensors are can be employed.

While specific embodiments of the invention have been shown anddescribed in detail to illustrate the application of the principles ofthe invention, it will be understood that the invention may be embodiedotherwise without departing from such principles.

What is claimed is:
 1. A method for determining a flow of a fluid, themethod comprising: positioning a vortex measuring device in the fluid;measuring under predetermined measuring device operating conditions anoutput from the vortex measuring device; deriving at least onetime-dependent signal from the output, the time-dependent signalcomprising a portion being representative of the flow, and a portionbeing representative of noise; determining, based on a frequencyspectrum of the signal, a value representative of the noise floor;identifying, based on the frequency spectrum of the signal, a peakcomponent; determining a relative peak criterion based on the determinedvalue representative of the noise floor; and if the peak componentsatisfies the relative peak criterion, determining the flow of the fluidby applying a predetermined algorithm to the frequency spectrum.
 2. Themethod according to claim 1, wherein the value representative of thenoise floor is calculated as an average of a number of selected noiserepresentative frequencies spectrum components.
 3. The method accordingto claim 2, wherein the noise representative frequencies spectrumcomponents are selected among frequency components below a predefinedfrequency value, wherein the noise representative frequencies spectrumcomponents are the remaining components after selecting a predeterminednumber of high value frequency components, wherein the high valuefrequency components are a predetermined number of frequency componentswith the highest amplitude values.
 4. The method according to claim 1,wherein a relative peak signal is determined from a difference betweenthe noise floor and the wanted signal, wherein the relative peak signalis larger than or equal to the relative peak criterion.
 5. The methodaccording to claim 1, wherein the predetermined algorithm is adaptivewith respect to the noise by: always calculating the relative peakcriterion as a function of the noise being present, and selecting asuitable averaging procedure securing a stable wanted signal output. 6.The method according to claim 1, wherein the frequency spectrum iscalculated by averaging frequency spectra of a predetermined number oftime dependent signals derived from the output of the measuring device.7. The method according to claim 1, wherein the measuring device is avortex measuring device, and wherein the time-dependent signal comprisesa portion being representative of a flow, and a portion beingrepresentative of noise.
 8. The method according to claim 1, wherein themeasuring device is a thermal flow meter comprising a plurality oftemperature sensors for measuring a thermal profile around a heatedbody.
 9. A method for determining flow of a vortex measuring device, themethod comprising the step of: computing a sequence of frequencyspectra, each frequency spectrum of the sequence being computed by themethod comprising averaging frequency spectra of a predetermined numberof time dependent signals derived from the output of the measuringdevice, wherein for the first spectrum of the sequence, thepredetermined number is 1, and for each subsequent spectrum of thesequence, the predetermined number is increased by a predeterminedpositive integer value, and wherein for each frequency spectrum of thesequence of frequency spectra, the corresponding flow is determined byapplying the predetermined algorithm; and evaluating if at least oneflow is determined to be greater than the noise floor, then the firstspectrum of the sequence of spectra where the determined flow is greaterthan the noise floor, is selected as basis for calculating the flow ofthe vortex measuring device, otherwise determining the flow to be zeroflow.
 10. The method of claim 9, wherein each frequency spectrum isdivided into a number of discrete frequency components, and identifyingthe peak component comprises the step of deriving the peak byinterpolating based on several frequency components.
 11. The method ofclaim 9, wherein: each frequency spectrum of the sequence of frequencyspectra is numbered with a consecutively increasing channel number, thefirst frequency spectrum being numbered with the smallest channelnumber, the last frequency spectrum being numbered with the largestchannel number, wherein frequency spectra having higher channel numbersbeing averaged over frequency spectra of predetermined numbers of timedependent signals with lower frequency limits than frequency spectra ofpredetermined numbers of time dependent signals, having lower channelnumbers; prior to the step of evaluating if at least one flow isdetermined to be greater than the noise floor, for each frequencyspectrum of the sequence of frequency spectra, the corresponding flow isdetermined by applying the predetermined algorithm, and if the flow ofthe spectrum having the smallest channel number is determined to begreater than the noise floor, that spectrum is selected as basis forcalculating a steady state flow of the vortex measuring device,otherwise if the flow of the spectrum having the smallest channel numberis determined to be equal to the noise floor and at least one of thespectra having a channel number between the smallest and the largestchannel number having a flow greater than the noise floor, one of thespectra having a flow greater than the noise floor is selected as abasis for calculating a dynamic flow of the vortex measuring device,otherwise the noise representative frequencies spectrum components areselected among frequency components below a predefined frequency value,wherein the noise representative frequencies spectrum components are theremaining components after selecting a predetermined number of highvalue frequency components, wherein the high value frequency componentsare a predetermined number of frequency components with the highestamplitude values.
 12. The method according to claim 9, furthercomprising a step of determining a stationary flow and a dynamic flow.13. The method according to claim 9, wherein the output signal is arelative pressure signal.
 14. The method according to claim 9, whereinthe vortex measuring device comprises a vortex sensor which is arrangedwithin a one-port housing.
 15. A vortex sensor comprising: a sensorconfiguration providing an output for deriving at least onetime-dependent signal, the at least one time dependent signal beingderived from the output under predetermined measuring device operatingconditions; evaluating means for evaluating a frequency spectrumrepresentative of the at least one time-dependent signal, thetime-dependent signal comprising a portion being representative of awanted flow signal, and a portion being representative of noise, whereinthe evaluating comprises the steps of: determining, based on thefrequency spectrum of the signal, a value representative of the noisefloor; identifying, based on the frequency spectrum of the signalderived under the predetermined operating condition, a peak component;determining a relative peak criterion based on the value representativeof the noise floor; and if the peak component satisfies the relativepeak criterion, determining the wanted flow signal by applying apredetermined algorithm.
 16. A vortex sensor according to claim 15,wherein the evaluating means is configured to compute a sequence offrequency spectra, each frequency spectrum of the sequence beingcomputed by the method comprising averaging frequency spectra of apredetermined number of time dependent signals derived from the outputof the measuring device, wherein for the first spectrum of the sequence,the predetermined number is 1, and for each subsequent spectrum of thesequence, the predetermined number is increased by a predeterminedpositive integer value, and wherein for each frequency spectrum of thesequence of frequency spectra, the corresponding flow is determined byapplying the predetermined algorithm and being configured to evaluate ifat least one flow is determined to be greater than the noise floor, thenthe first spectrum of the sequence of spectra where the determined flowis greater than the noise floor, is selected as basis for calculatingthe flow of the vortex measuring device, otherwise to determine the flowto be zero flow.
 17. A vortex sensor according to claim 15, wherein thevalue representative of the noise floor is calculated as an average of anumber of selected noise representative frequencies spectrum componentsand the noise representative frequencies spectrum components areselected among frequency components below a predefined frequency value,wherein the noise representative frequencies spectrum components are theremaining components after selecting a predetermined number of highvalue frequency components, wherein the high value frequency componentsare a predetermined number of frequency components with the highestamplitude values.
 18. A vortex sensor according to claim 15, wherein arelative peak signal is determined from a difference between the noisefloor and the wanted signal, wherein the relative peak signal is largerthan or equal to the relative peak criterion.
 19. A vortex sensoraccording to claim 15, wherein the predetermined algorithm is adaptivewith respect to the noise by: always calculating the relative peakcriterion as a function of the noise being present, and selecting asuitable averaging procedure securing a stable wanted signal output. 20.A vortex sensor according to claim 15, wherein the frequency spectrum iscalculated by averaging frequency spectra of a predetermined number oftime dependent signals derived from the output of the measuring device.